Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
Russian
bert
pretraining
russian
fill-mask
embeddings
masked-lm
tiny
feature-extraction
text-embeddings-inference
Instructions to use cointegrated/rubert-tiny2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cointegrated/rubert-tiny2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cointegrated/rubert-tiny2") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use cointegrated/rubert-tiny2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("cointegrated/rubert-tiny2") model = AutoModelForPreTraining.from_pretrained("cointegrated/rubert-tiny2", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download .gitignore from cointegrated/rubert-tiny2: direct link, hf CLI and curl.
- Browser
- Download file 6 Bytes
-
https://huggingface.co/cointegrated/rubert-tiny2/resolve/main/.gitignore
- Command line
-
hf download hf://cointegrated/rubert-tiny2/.gitignore
-
curl -L -o .gitignore https://huggingface.co/cointegrated/rubert-tiny2/resolve/main/.gitignore
6 Bytes
| .idea | |